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Record W2266111362 · doi:10.3305/nh.2015.32.sup2.10317

Promoting the right nutrition and hydration in schools by community nursing.

2015· article· en· W2266111362 on OpenAlexaboutno aff
J. Mateo Segura, R. Arquero Jerónimo, Mm. Acosta Amorós

Bibliographic record

VenuePubMed · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Healthy eatingMedical educationPsychologyHealth professionalsNutrition EducationNursingMedicineGerontologyHealth carePhysical activity

Abstract

fetched live from OpenAlex

Method: We conducted a qualitative descriptive study by two nurses from a health center of Cartagena on 40 Primary pupils of two schools in Cartagena during 30-31 March 2015 to train teachers and students on healthy nutrition-hydration so as to assess previous and acquired knowledge by the students. Data were obtained through an open question survey about nutrition, hydration, balanced diet and healthy eating prior to a talk and various educational games (food pyramid, drawings, plasticine games...) to strengthen knowledge. In the second day of school we conducted the same survey, assessing their recently learned skills. Teachers were in charge of strengthening the information provided during the school quarter, revealing in previous surveys that were conducted in the classrooms a basic knowledge with a few trends. Results: They didn´t know which foods are healthy or not and the minimum daily liquid amount required. Some children considered bakery as a must in breakfast and dinner. 95% of students improved their knowledge about nutrition. Conclusions: Educational activities should be included within existing health programs in all schools. Health education guided by healthcare professionals is better captured by the students at an early age, because is in this age is when they will shape their eating habits and lifestyles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.115
GPT teacher head0.400
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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